Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Explores deep learning fundamentals, including image classification, neural network working principles, and machine learning challenges.
Covers clustering, semi-supervised clustering, and binary classification formalization, along with various classification techniques.
Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.
Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.
